IMOD Innovate • Grow • Prosper • Use of High Science Tools in Integrated Watershed Management Proceedings of the National Symposium
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ISBN: 978-92-9066-540-3 CPE 169 241-2011
Use of H
igh Science Tools in Integrated W
atershed Managm
ent
Contact InformationICRISAT-Patancheru(Headquarters)Patancheru 502 324Andhra Pradesh, IndiaTel +91 40 30713071Fax +91 40 [email protected]
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About ICRISAT
www.icrisat.org
The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) is a non-profit, non-political organization that conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. Covering 6.5 million square kilometers of land in 55 countries, the semi-arid tropics have over 2 billion people, and 644 million of these are the poorest of the poor. ICRISAT and its partners help empower these poor people to overcome poverty, hunger, malnutrition and a degraded environment through better and more resilient agriculture.
ICRISAT is headquartered in Hyderabad, Andhra Pradesh, India, with two regional hubs and four country offices in sub-Saharan Africa. It belongs to the Consortium of Centers supported by the Consultative Group on International Agricultural Research (CGIAR).
IMOD Innovate • Grow • Prosper •
Use of High Science Tools in Integrated Watershed ManagementProceedings of the National Symposium
Organizing CommitteeCo-Chairs SP Wani
Prabhat Kumar
Members P PathakKaushal GargArun PalKNV Satyanarayana
Secretarial Support
Y Prabhakara RaoJyoti SharmaN Sri Lakshmi
Citation: Wani SP, Sahrawat KL and Kaushal K Gard (eds.). 2011. Use of High Science Tools in Integrated Watershed Management. Proceedings of the National Symposium, 1–2 Feb 2010, NASC Complex, New Delhi, India. Patancheru 502 324, Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics for the Semi-Arid Tropics. ISBN 978-92-9066-540-3. CPE 169. 328 pp.
© International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), 2011. All rights reserved.
ICRISAT holds the copyright to its publications, but these can be shared and duplicated for non-commercial purposes. Permission to make digital or hard copies of part(s) or all of any publication for non-commercial use is hereby granted as long as ICRISAT is properly cited. For any clarification, please contact the Director of Communication at [email protected]. ICRISAT’s name and logo are registered trademarks and may not be used without permission. You may not alter or remove any trademark, copyright or other notice.
AcknowledgementWe sincerely thank Department of Land Resources (DoLR), Ministry of Rural Development, Government of India, for sponsoring the symposium. We are grateful to National Bank for Agriculture and Rural Development (NABARD), Sir Dorabji Tata Trust (SDTT), Sir Ratan Tata Trust (SRTT) for co-sponsoring the event. We thank the help of Mr Prabhat Kumar, Director, Business and Country Relations, ICRISAT Liaison Office, for coordinating the workshop. We thank Ms N Shalini for language editing; Mr KNV Satyanarayana, Mr Arun Pal and Ms Jyothi for administrative support; Mr Y Prabhakar Rao and Ms N Sri Lakshmi for logistical support; and Communication Office, ICRISAT for production of this report.
2011
Use of High Science Tools in Integrated Watershed Management
Proceedings of the National Symposium1–2 February 2010
NASC Complex, New Delhi, India
EditorsSP Wani, KL Sahrawat and Kaushal K Garg
Organized by
in collaboration with
Department of Land ResourcesMinistry of Rural Development, Government of India
Sponsored bySir Dorabji Tata Trust (SDTT)Sir Ratan Tata Trust (SRTT)Mumbai, Maharashtra, India
and
National Bank for Agriculture and Rural DevelopmentMumbai, Maharashtra, India
Objectives of the Symposium
Contents
Suhas P Wani, AVR Kesava Rao and Kaushal K Garg .................. 1
B Venkateswarlu, KV Rao, Kaushalya Ramachandran and UK Mandal ............................................................................. 49
Rita Teaotia and Ram Kumar ....................................................... 66
Alok K Sikka, DR Sena, VN Sharda and RS Kurothe ................... 90
K Palanisami, D Suresh Kumar and Suhas P Wani ................... 106
BS Das ....................................................................................... 127
AVR Kesava Rao, Suhas P Wani and Piara Singh ..................... 145
PS Roy, T Ravisankar and K Sreenivas ..................................... 156
PG Diwakar and SG Mayya ........................................................ 179
KV Raju, M Babu Rao, KV Sarvesh, NC Muniyappa, Abhijit Dasgupta and Suhas P Wani ...................................................... 195
PK Joshi, Suhas P Wani, KH Anantha and AK Jha .................... 217
Kaushal K Garg and Suhas P Wani ............................................ 241
Prabhakar Pathak, R Sudi and Suhas P Wani ........................... 253
P Biswabandhu Mohanty ............................................................ 276
K Boomiraj, Suhas P Wani and PK Aggarwal ............................. 292
305
307
311
1
Harnessing New Science Tools through IWMP to Unlock Potential of Rain-fed Agriculture
Suhas P Wani, AVR Kesava Rao and Kaushal K Garg
AbstractSemi-Arid Tropics (SAT) are characterized by highly variable rainfall, poor soils, low yields and poor developmental infrastructure. Watershed management is now an accepted strategy for development of rain-fed agriculture in these areas. New science tools like remote sensing, geographical information systems (GIS), water balance, simulation modeling, information and communication technology (ICT) are currently being used very widely in irrigated and well-endowed areas. Importance of these tools in the SAT areas is now well understood and recognized. Application of new science tools in rain-fed agriculture opens up new vistas for development through integrated watershed management programs (IWMP). ICRISAT in partnership with national agricultural research systems and advanced research institutes in Asia has applied new science tools for enhancing the productivity of rain-fed systems in the SAT through science-led development.
The remarkable developments in space technology currently offers satellites, which provide better spatial and spectral resolutions, more frequent revisits, stereo viewing and on board recording capabilities. High spatial and temporal resolution satellite data could be effectively used for watershed management and monitoring activities at land ownership level. Techniques are also successfully used for preparing detailed thematic maps, watershed development plans and continuous monitoring of the natural resources in rain-fed areas. Synergy of GIS and Web Technology allows access to dynamic geospatial watershed information without burdening the users with complicated and expensive software.
Use of smart sensor network along with GIS, RS, simulation modeling and ICT opens up new opportunities for developing intelligent watershed management information systems. These tools can help in improving the rural livelihoods and contribute substantially to meet the millennium development goals of halving the number of hungry people by 2015 and achieving food security through enhanced use
tropical countries.
2
Introduction
in situ
3
2
New Science Tools for Integrated Watershed Management
Geographic Information System (GIS)
4
5
Remote Sensing
Sources of the Results
6
Table 1. Suitability of various RS sensors in watershed studies.
7
Crop-Growth Simulation Modelling
9
factorial combination
ex ante
Figure 1. Simulated potential, experimental and province mean pod yields and yield gap of rain-fed groundnut in (a) spring and (b) autumn-winter seasons at selected sites in northern Vietnam.
10
Field Sensors and Data Communication Devices
Global Positioning System (GPS)
Automatic Weather Station (AWS)
11
Mobile devices
Cellular phones
12
PDA phone with GPS
Data Storage and Dissemination
13
Application of Spatial Technologies in Rain-fed Agriculture and Watershed Management
Characterization of Production Systems in India
2 2
14
Land use Mapping for Assessing Fallows and Cropping Intensity
, season
Figure 2. Distribution of different soil orders in the production systems in India.
15
Figure 3. A close view of WiFS images of part of Vidisha district, Madhya Pradesh, during mid-rainy, late-rainy and post-rainy seasons
16
season
Figure 4. Spatial distribution of various land use and land cover categories in Madhya Pradesh.
17
Oryza sativa
Spatial Distribution of Rainy Season Fallows in Madhya Pradesh
kharifrabi
rabi
Figure 5. Spatial distribution of rainy season fallows in districts of Madhya Pradesh.
19
rabi
South Asia – Potential for Legumes
20
Vigna radiata; Vigna mungo
Lens culinaris khesari Lathyrus sativus Vicia faba Pisum
sativum
Figure 6. Spatial distribution of rice-fallows in Indo Gangetic Plains of South Asia.
21
Rhizobium
GIS Mapping of Spatial Variability of Soil Micronutrients at District Level
22
Figure 7. Availability of phosphorus in selected districts of Karnataka.
23
taluk
taluk
Assessment of Seasonal Rainfall Forecasting and Climate Risk Management Options for Peninsular India
24
Baseline Studies to Delineate Watershed
Figure 8. Satellite Data and DEM of watershed in part of Nalgonda district, Andhra Pradesh.
25
Watershed characterization
Prioritisation of watersheds
26
Regional-Scale Water Budgeting for SAT India
27
Spatial Water Balance Modeling of Watersheds
Water Balance of Different Water Intervention Scenarios
Sediment Transport and Soil loss
Figure 9. Water balance for the four different water management scenarios for
29
Integrated Watershed Management for Land and Water Conservation and Sustainable Agricultural Production in Asia
Assessment of Agroclimatic Potential
vis-à-vis
development).
30
Climatic Water Balance
Climatic water balance of watersheds in China, Thailand, Vietnam and India:
31
Rain-fed LGP
Table 2. Annual water balance characters (all values in mm)
32
Drought Monitoring at Watersheds
33
Weather Forecasting for Agriculture
Figure 11. Drought monitoring at benchmark watersheds in Andhra Pradesh during 2004.
34
Watershed Monitoring
Satellite Images for Impact Assessment
35
kharif
Monitoring and Evaluation of Watersheds using Remote Sensing
36
Figure 12. Guna watershed, Madhya Pradesh.
37
Monitoring and Impact Assessment of Adarsha Watershed
Gossypium
Figure 13. Landuse and cropping pattern of Adarsha watershed, Kothapally, Andhra Pradesh.
39
Figure 14. Thematic maps depicting soils and land use plan in Adarsha watershed, Kothapally, Andhra Pradesh.
40
Spatial Simulation Modelling
41
42
Use of ICT in Watershed Management
Figure 15. Information and communication technology services enabled at Addakal, Mahabubnagar district, Andhra Pradesh, India.
Summary
43
ReferencesAllen RG, Pereria LS, Dirk Raes Martin Smith.
All India Soil and Land Use Survey.
44
Anonymous.
Chuc NT, Piara Singh, Srinivas K, Ramakrishna A, Chinh NT, Thang NV, Wani SP, Long TD.
Cooper P, Rao KPC, Singh P, Dimes J, Traore PS, Rao K, Dixit P Twomlow SJ.
Diwakar PG Jayaraman V.
Dwivedi RS, Ramana KV, Wani SP Pathak P.
in
FAO .
Garbrecht J Martz LW.
Guyot G. in
Harris D, Joshi A, Khan PA, Gothkar P Sodhi PS.
ICRISAT
45
Johansen C. in
Kaushalya Ramachandran, Mandal UK, Sharma KL, Gayatri M, Baskar V, Venkatravamma K Kartik P.
Kaushalya Ramachandran, Mandal UK, Sharma KL Venkateswarlu B.
Kasturirangan K, Aravamudam R, Deekshatulu BL, Joseph G Chandrasekhar MG.
Keig G McAlpine JR.
Kesava Rao AVR, Wani SP, Singh P, Irshad Ahmed M Srinivas K.
Khan MA, Gupta VP Moharana PC.
Khare YD, Srivastara NT, Deshpande AS, Tamhane RM Sinha AK.
In
Krishna Murthy YVN, Srinivasa Rao S, Prakasa Rao DS Jayaraman V.
46
Navalgund RR, Parihar JS, Venkataratnam L, Krishna Rao MV, Panigrahy S, Chakraborthy M, Hebbar KR, Oza MP, Sharma SA, Bhagia N Dadhwal VK.
NRSA
NRSA
NRSA
Obi Reddy GP, Maji AK, Srinivas CV Gajbhiye KS.
In
Rao BRM, Sreenivas K, Fyzee MA Ravi Sankar T.
Rao GGSN, Kesava Rao AVR, Ramakrishna YS Victor US.
Rao VN, Singh P, Hansen J, Giridhara Krishna T Krishna Murthy SK.
in
Rao KV, Venkateswarlu B, Sahrawat KL, Wani SP, Mishra PK, Dixit S, Srinivasa Reddy K, Manoranjan Kumar Saikia US (Eds.).
47
Roy PS, Ravisankar T Sreenivas K.
Sahrawat KL, Wani SP, Rego TJ, Pardhasaradhi G Murthy KVS.
Sahrawat KL., Rego TJ, Wani SP Pardhasaradhi G.
Saxena RK, Verma KS, Chary GR, Srivastava R Barthwal AK.
Sekhar KR Rao BV.
Sharma T.
Singh P, Aggarwal PK, Bhatia VS, Murty MVS, Pala M, Oweis T, Belni B, Rao KPC Wani SP.
In ain
Sreedevi TK, Wani SP, Kesava Rao AVR, Singh P Ahmed I. 2009.
Srivastava PK, Srinivasan TP, Gupta A, Singh S, Nain JS, Amitabh, Prakash S, Kartikeyan B Gopala Krishna B.
Subbarao GV, Kumar Rao JVDK, Kumar J, Johansen C, Deb UK, Ahmed I, Krishna Rao MV, Venkataratnam L, Hebbar KR, Sai MVSR Harris D.
Thakkar AK Dhiman SD.
Thornthwaite CW Mather JR.
Wani SP. In
Wani SP, Singh HP, Sreedevi TK, Pathak P, Rego TJ, Shiferaw B Shailaja Rama Iyer.
. In r
Wani SP, Joshi PK, Raju KV, Sreedevi TK, Wilson JM, Amita Shah, Diwakar PG, Palanisami K, Marimuthu S, Jha AK, Ramakrishna YS, Meenakshi Sundaram SS Marcella D’Souza.
Wani SP, Singh P, Boomiraj K Sahrawat KL.
49
Application of Geomatics in Watershed Prioritization, Monitoring and
Evaluation – CRIDA’s ExperienceB Venkateswarlu, KV Rao, Kaushalya Ramachandran
and UK Mandal
AbstractWatershed-based development has been the prime strategy for rain-fed regions of India since 1980s to conserve natural resources, enhance agricultural production and improve rural livelihoods. Although soil and water conservation was initially the primary objective of watershed program that saw large public investments since inception, its focus later shifted to people’s participation, equity and livelihood security, particularly from the mid nineties. One of the major goals of the watershed program is also regeneration of degraded lands. Many of these interventions need modern tools like GIS and remote sensing so that planning, prioritization and monitoring becomes more science based and the methodology and approaches can become universally applicable.
Application of GIS, remote sensing and use of GPS for monitoring and evaluating watershed projects is a recent development. Two exercises in this direction were initiated in CRIDA wherein relevant sustainability
these tools and the outcome of these studies have been presented in this paper. The bio-physical parameters were temporally evaluated from two standpoints – the pre- and the post-project implementation
and changes in NDVI and land cover were analyzed to assess if agricultural development within treated watersheds were sustainable.
Utility of Geomatics for developing criteria for watershed selection, as indicated in various guidelines including the recent Common Guidelines of 2008, cannot be overemphasized. Major bio-physical parameters that include potential runoff and soil erosion besides
indicators for monitoring and evaluation in the post-project phase. Lack of information on these parameters at watershed - level, as evidenced earlier have been made-up to a large extent through application of
50
better resolution data and generation of surrogate indicators. With the availability of DEM datasets in public domain and with the availability of GIS software, it is now possible to estimate certain parameters that have a direct bearing on potential runoff and soil loss, thus providing a scope for characterization of watersheds as mentioned earlier. The paper also presents an example of use of DEM dataset in GIS environment for prioritization of watersheds based on runoff -potential and soil loss parameter at the district- level.
Introduction
Application of Geomatics in CRIDA Watershed Program
51
.
Resource Inventory and Planning for Technology Upscaling
Watershed Delineation and Prioritization
52
Figure 1. Characterization of NRM status using Geomatics.
Case Study
53
Based on drainage density
Based on hypsometric integral (erosion potential is known qualitatively)
Figure 2. Geomorphological characterization of watersheds for prioritization – a case study of Mahabubnagar district.
54
H
55
L
L
Table 1. Estimation of priority area for treatment based on erosion potential – prioritization and decisions on SWC interventions based on higher HI.
56
Soil Quality Assessment using GIS and RS:
Table 2. Estimation of priority area for treatment based on runoff potential – higher drainage density – more water harvesting.
57
Satellite Imagery
rabirabi
Figure 3. Satellite images with varying ground resolution – Sakaliseripalli watershed.
kharif
Soil Health Report
Soil Quality Index
Figure 4. Thematic maps of soil quality, agricultural productivity and soil loss potential of Sakaliseripalli watershed.
59
On Farm Experiment
kharif
2O5
Evaluation of Watershed Development Program under ICAR National Fellow Scheme at CRIDA
60
Table 3. List of sustainability indicators constructed for the study.
61
Conclusions
62
ReferencesKatyal JC, Kaushalya Ramachandran, Narayana Reddy M Rama Rao CA.
Katyal JC, Kaushalya Ramachandran, Narayana Reddy M, Mahipal Ram Mohan I.
Figure 5. Assessing temporal variations in LULC & degradation.
63
Kaushalya Ramachandran.
Kaushalya Ramachandran, Gayatri M, Bhasker V, Srinivas G, Venkatravamma K, Srinivas T Sankar Rao M.
Indian J. Dryland Agric Res. & Dev
Figure 6. Estimating seasonal variations in crop vigour using NDVI as a indicator.
64
65
Kaushalya Ramachandran, Mandal UK Sharma KL, Gayatri M, Baskar Venkatravamma K Kartik P.
Kaushalya Ramachandran, Mishra PK Padmanabhan MV.
Kaushalya Ramachandran, Mandal UK, Sharma KL Venkateshwarlu B.
Mandal UK.
Velatytham M, Mandal DK, Mandal C Sehgal JL.
Vittal KPR.
66
Use of Hi-Science Tools in IWMPRita Teaotia and Ram Kumar
Abstract Gujarat has been at the forefront of watershed development program in the country, both in terms of quantity and quality. By the end of the year 2008, more than 8000 micro-watershed projects involving more than Rs.25000 million have been either completed or on going in the state. However, a lot remains desired in overall project planning, implementation and post project management so as to make the program sustainable. Considering the different concerns regarding project management, the New Common Guidelines for Watershed Program has prescribed for use of high science tools like application of Remote Sensing & Geographic information System (GIS), Management Information System (MIS) and other Information & Communication Technologies.
Accordingly, the Government of Gujarat has undertaken the Integrated Watershed Management Program (IWMP) incorporating the available high science tools; GIS is a major part of the whole process. The Gujarat State Watershed Management Agency (GSWMA), the nodal agency at the state level for IWMP in collaboration with Bhaskaracharya Institute of Space Applications and Geo-informatics (BISAG) has taken initiative in this regard by integrating GIS based data at both micro and macro level planning.
Introduction
1
67
Background
Prioritization of the watersheds:
Technical inconsistencies:
People vs. technical experts dilemma:
Preparation of detailed project report:
Monitoring and evaluation:
Impact assessment:
69
Watershed Development in Gujarat
Use of GIS in Watershed Management
70
71
Planning
Table 1. Various GIS datasets used and their sources.
72
Box-1: Excerpts from Common Guidelines 2008
73
taluk
Prioritization
74
Figu
re 1
. Pla
nnin
g fro
m m
acro
leve
l to
the
mic
ro le
vel.
75
76
Tabl
e 2.
Crit
eria
& w
eigh
tage
for p
riorit
izat
ion
set b
y D
oLR
.
77
Figure 2. Various spatial layers.
79
Figure 3. Final prioritization.
Figure 4. Map depicting the planned areas for the whole of 18 years.
Figure 5. The prioritization model of watershed management.
Developmental Planning
Table 3. Action plan for forest land.
DPR Preparation
Table 4. Action plan for wasteland
Table 5. Action plan for agriculture land
Table 6. Planned activities for watershed development under IWMP.
Convergence
Figure 6. Possibilities for convergence.
gram sabha
Case Study: Convergence
taluk
Implementation
Monitoring and Evaluation
Impact Assessment
Conclusion
Box-2: Case Study: Impact assessment of watershed development in Idar Taluka (Code: 5F2D4b4)
Acknowledgement
ReferencesGovernment of India.
Government of India.
Government of India.
NREGA.
Joint Convergence Guideline.
Watershed Atlas. 1990
http://cgwb.gov.in/watershed/about-ws.html
http://www.esri.com/what-is-gis/index.html
Watershed Management-T V R Murthy, New age publication, 1995
90
Use of Modeling in Watershed PlanningAlok K Sikka1, DR Sena3, VN Sharda2 and RS Kurothe3
AbstractIntegrated watershed management has emerged as a powerful concept in development planning for agriculture and rural development in India. Importance of integrated planning of natural, animal and social resources for enhanced productivity and livelihood is evident from the increased outlay of watershed programs in the XIth Five Year Plan. Comprehensive Assessment of Watershed Programs in India by the
planned and systematically implemented watershed projects. Now all the watershed programs of different ministries/departments are being implemented following the new Common Guidelines for Watershed Development Projects effective from April 2008. The guidelines emphasize using new science and technology inputs, including Remote Sensing (RS), Geographic Information System (GIS) and modeling to bring about a paradigm shift in preparing detail project reports (DPRs) for implementation of the watershed development programs.
Integrated watershed planning is done on the basis of its resource inventory, which includes the analysis of the present status or conditions (i.e., bench mark analysis) of its natural resources (soils, topography, drainage, land use, water resources, forest, vegetation, etc.), animal/livestock resources, socio-economic and livelihood conditions and human resources and the type/extent of problem and needs of the watershed area and the community. Modeling application requires spatial data at watershed scale. Creation of a spatial database is
maps and baseline data in place, followed by spatial analysis using analytical tools to help identify special features, problems, needs and
of the watershed.
The advances in remote sensing in collecting spatially variable data at higher resolution, GPS and capabilities of GIS in storing, retrieving and
91
manipulating data have shown tremendous potential in circumventing problems of the conventional and time-taking techniques in watershed planning. Integrating and collating data from multiple sources, conventional and/or remote sensing and others, with GIS, can lead to important operational applications including better opportunities for use of modeling in watershed planning.
Use of modeling as a tool in conjunction with spatial data manipulation in GIS for estimating runoff, soil erosion and sedimentation, land
needing treatment within the watershed for optimized investments, planning, location and design of various soil and water conservation, water harvesting and other such interventions, and analysis of best management practices (BMPs) in preparing watershed plan, has been presented in the paper. Opportunities of using data from free sources in watershed planning have also been presented. Use of distributed modeling to address issue of upstream–downstream
are also discussed. Need for identifying and integrating the analytical biophysical and socio-economic models to GIS through user interface in a modular modeling frame work to develop decision support systems (DSS) and web-enabled system is emphasized to help automate the watershed planning process and simulate the effects of various watershed intervention scenarios.
Introduction
92
Watershed Planning A watershed plan
93
Requisite Information for Watershed Planning
94
:
Figure 1. A schematic of watershed management planning process.
95
Watershed Modeling as a Planning Tool
96
Figure 2. Schematic of popular modeling environments SWAT and KINEROS2 to determine various watershed functions essential for watershed planning (Burns et al. 2004).
KINEROS Outputs SWAT Outputs3
3
3
97
.
Decision Support Model for Alternate Management Strategies
.
Figu
re 3
. Pro
cess
bas
ed m
odel
ing
for i
dent
ifyin
g cr
itica
l are
as fo
r int
erve
ntio
n (e
.g. a
mod
el S
WAT
out
put).
99
Upstream-Downstream Relationship
Watershed Information System (NWIS)
Figure 4. Treatment of partial (critical) area and its effect on soil loss in KG-4-1 watershed of Nilgiris.
100
Opportunities for Watershed Planning Using free Resources
101
Tabl
e 1.
Con
cept
ual f
ram
ewor
k fo
r Nat
iona
l Wat
ersh
ed In
form
atio
n Sy
stem
102
Conclusions
Figure 5. GIS (free open source) delineated contours derived from ASTER data draped in Google earth and a 3D DEM derived from it. (Prepared by D R Sena as an example).
103
ReferencesAnonymous.
Figure 6. Google resources translated into useful baseline information for DPR preparation (CSWCRTI, 2008).
104
Anonymous.
Arnold JG, Williams JR, Srinivasan R, King KW Griggs RH.
Bingner RL Theurer FD. Internet http://www.ars.usda.gov/Research/docs.htm?docid=5199
Burns IS, Scott S, Levick L, Hernandez M, Goodrich DC, Miller SN, Semmens DJ Kepner WG.
Version 1.4.
Chansheng He, Changan Shi, Changchun Yang and Bryan Agosti P.
CSWCRTI.
Jin-Yong Choi, Bernard Engel A Richard Farnsworth L.
Lahlou LM, Bryer M, Kumar D Kratt K.
Lim KJ, Engel B, Kim Y, Bhaduri B Harbor J.
Samra JS Sharma KD.
Semmens D, Goodrich DC, Unkrich CL, Smith RE, Woolhiser DA Miller SN.
Sikka AK Paul DK. In
105
Sunday Tim U Mallavaram S.
Woolhiser DA, Smith RE Goodrich DC. KINEROS, A kinematic
Yates D, Sieber J, Purkey D Huber-Lee A.
106
Application of Econometic Methods for Assessing the Impact of Watershed Programs
K Palanisami1, D Suresh Kumar2 and Suhas P Wani3
1
2
3
Abstract Watershed programs in India are contributing to water resources development, agricultural production and ecological balance. Impact
a framework to identify what impacts to assess and (ii) developing a framework to look after the indicators together and assessing the overall impact of the project. The nature of watershed technologies and their impact on different sectors pose challenges to the evaluation
methodologies, (ii) selection of indicators, (iii) choice of discount rate,
the impacts in an isolated manner. In order to evaluate the impacts of watershed programs in a holistic manner, the Economic Surplus (ES) approach has been applied. The economic surplus incorporates both consumer surplus and producer surplus. The consumer surplus
product for a price that is less than they would be willing to pay. The
market price mechanism that is higher than they would be willing to sell for. In the case of watershed programs, producers are mainly the
watershed interventions such as soil and moisture conservation, water table increase and livestock improvement activities and consumers are mainly the other stakeholders in the region, viz. non-farm households representing the labourers, business people and people employed in non-agricultural activities. The ES method is demonstrated using the data from a cluster of 10 watersheds in the Coimbatore district of Tamil Nadu. The distributional effects of watershed programs are also captured through the ES method. The results of the conventional method had indicated that the BCR is 1.23, IRR is 14% and NPV is Rs 567912. The results of the ES method had indicated that the BCR is 1.93, the IRR is 25 % and the NPV is Rs 2271021. The conventional
107
evaluation method had thus underestimated the watershed impacts. Hence, possibilities of using the ES methodology in the future watershed evaluation programs could be examined.
Introduction
et al.
109
110
Methodology
Economic Surplus Approach
et al.,
111
Theoretical Framework
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P1,Q1
Q1 P1 P0abP1
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0 )PP(cs lo0 (1)
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Figure 1. Graphical representation of economic surplus method.
113
Application of Economic Surplus Method to Watershed Evaluation
114
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P0, A0 Y0
Cost of Project
Study Area and Data Collection
116
Figure 2. Map of the study area.
117
Table 1. Details of watersheds covered for the study in Coimbatore district of Tamil Nadu.
Data
et al
Results and Discussion
Table 2. General characteristics of sample farm households.
119
3
3 3
120
NPPIECN
iiNi /11*log...
1
121
Application of Economic Surplus Method
Table 5. Impact of watershed development intervention on yield and cost.
122
Table 6. Impact of watershed development activities on the village economy.
TS = CS + PS = P0Q0K(1+0.5ZCS = P0Q0Z(1+0.5ZPS = P0Q0(K–Z)(1+0.5Z
123
Conclusions and Policy Recommendations
Table 7. Results of economic analysis employing economic surplus method.
124
Acknowledgements
References
125
Department of Land Resources ,
Kerr John, Pangare Ganesh, Pangare VL George PJ. ,
Libardo Rivas R, García James, Seré v, Carlos, Lovell S Jarvis, Sanint Luis R Pachico Douglas.
Logesh GB.
Maredia Mywish, Byerlee Derek Anderson Jock.
Moore, Michael R, Gollehon Noel R Hellerstein Daniel M.
Pachico D, Lynam JK Jones PG.
Palanisami K, Suresh Kumar D Balaji P.
Palanisami K and Suresh Kumar D.
.
Palanisami K Suresh Kumar D.
Rao CH.
126
Swinton SM. 2
Wander, Alcido Elenor, Magalhaes, Marilia Castelo Vedovoto, Graciela Luzia Martins, Espedito Cezario.
Wani SP, Joshi PK, Raju KV, Sreedevi TK, Wilson JM, Shah Amita, Diwakar PG, Palanisami K, Marimuthu S, Jha AK, Ramakrishna YS, Meenakshi Sundaram SS D’Souza Marcella.
127
Recent Developments in Vadose Zone Hydrology: Opportunities and Challenges for Sustainable Utilization of Water and Nutrients
for Enhancing ProductivityBS Das
AbstractDeclining total land area under cultivation, increasing demand for land for non-agricultural use, demand for food grains, large gap between actual and potential yields, and recent trends in weather anomalies call for an urgent action to identify ways and means for improving
nutrients has a potential opportunity to improve agricultural productivity
as rice are in the order of 25%. For the past two decades, several developments in vadose zone hydrology have opened opportunities
sensors and user-friendly simulation models are becoming reliable aids for making informed agricultural decisions on how much and
crops, making precision farming a reality. A summary of these two products are discussed in this document with the objective to identify
Introduction
129
130
131
Conceptual Developments in Flow and Transport Modeling
HYDRUS Suite:
132
Soil Water Assessment Tool (SWAT):
133
MIKE SHE:
134
Developments in Measurement Techniques
135
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L
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fZ120
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Z0 L ZL
)]25(02.01/[1 TfT
Zcable
Ztotal
136
Combined Heat Pulse and Resistivity Technology:
k C
Tm C
mTreqC 2
qr C
q Tm
wsb CcC
Cw bcs
137
Imaging Spectroscopy:
Application of Measurement and Modeling Techniques for Productivity Enhancement and Resource Conservation – A Case Study at IIT Kharagpur
139
kharif
im m
wm
m SzhhK
zt1)(
wim
im St
)( immw hh
140
S
rim rm sm sim n Ks
rm rim
sm sim
Ks
Implications for Productivity Enhancement
141
ReferencesAbbott MB, Bathurst JC, Cunge JA, O’Connell PE Rasmussen J.
Arnold JG, Srinivasan R, Muttiah RS Williams JR.
Baumgardner MF, Silva LF, Biehl LL Stoner R.
Ben-Dor E., Goldshleger N, Benyamini Y, Agassi M Blumberg D.
Ben-Dor E, Chabrillat S, Demattê JAM, Taylor GR, Hill J, Whiting ML Sommer S.
Campbell GS, Calissendorff C Williams JH.
Carlson TN, Gillies RR Schmugge TJ.
Chabrillat S, Goetz AFH, Krosley S Olsen HW.
Clark RN. In
Corwin DL Lesch SM.
471
142
Dalton FN, Herkelrath WN, Rawlins DS Rhoades JD.
Das BS, Wraith JM Inskeep WP.
Demattê JAM, Toledo AMA Simões MS.
FAOSTAT.
Fox GA and Metla R.
Galvao LS, Formaggio AR, Couto EG Roberts DA.
Garg KK.
Garg KK, Das BS, Safeeq Md Bhadoria PBS.
Giese K Tiemann R.
Harter T Hopmans JW.
Heimovaara TJ, Focke AG, Bouten W Verstraten JM.
Lagacherie P, Baret F, Feret JB, Madeira Netto J Robbez-Masson JM.
143
Ledieu J, de Ridder P, de Clerck P Dautrebande S.
Leone AP Sommer S.
Mathieu M Pouget, Cervelle B Escadafal R.
Nimmo JR.
Patil MD, Das BS, Barak E, Bhadoria PBS Polak A.
Patil MD.
Raun WR Johnson GV.
Refsgaard J Storm.
Rhoades JD, Manteghi NA, Shouse PJ Alves WJ.
Risler PD, Wraith JM Gaber HM.
Robinson DA, Jones SB, Wraith JM, Or D Friedman SP.
144
Santra P, Sahoo RN, Das BS, Samal RN, Pattanaik AK Gupta VK.
Shrestha RP.
Simunek J, Sejna M Van Genuchten M Th.
Simunek J, Jarvis NJ, Van Genuchten M Th, Gardenas A.
Swaminathan MS.
Topp GC, Davis JL Annan AP.
Tuong TP and Bhuiyan SI.
U.S. Salinity Laboratory Staff.
Vagen TG, Shepherd KD Walsh MG.
Viscarra Rossel RA McBratney AB.
Wopereis MCS, Bouman BAM, Kropff MJ, Berge HFMT and Maligaya AR.
145
Use of Agroclimatic Datasets for Improved Planning of Watersheds
AVR Kesava Rao, Suhas P Wani and Piara Singh
AbstractMaximizing agricultural production from rain-fed areas in a sustainable manner is the need of the day to feed the ever-increasing population. Integrated watershed management with focus on productivity enhancement and livelihood improvement is one of the high priority
whole. Reliable and long-term data on agroclimate, soils, crop varieties and crop production at taluk/block/district-level for several years are needed for undertaking climatic analyses and to understand variations in agricultural productivity and changes in the cropping patterns. Data on crop phenology, growth and yield characters are needed to quantify crop-weather relationships and for validating crop-growth simulation models. Agroclimatic datasets need to be developed at individual watershed level and climatic analyses help in assessing rainwater
of crops, risk analysis of climatic hazards, adoption of farming methods and choice of farm machinery. In this paper, results of climatic analysis of selected watersheds in India with respect to water balance and length of rain-fed crop-growing period, yield gap analysis of some important crops are presented and discussed. Use of agroclimatic datasets goes much beyond agroclimatic analysis of watersheds. Current issues like end-of-the-season crop yield forecasting, climatic change impact assessment, crop insurance to farming community, maintaining quality of produce to compete with international market, sustainability of the yield and environment are also to be addressed. Enhancing climate awareness among the rural stakeholders using new IT tools is the need of the hour.
Introduction
146
vis-à-vis
Agroclimatic Datasets and Database Management
taluk
147
Water Balance
3
3
149
Climate Change Impacts on LGP
150
Yield Gap Analysis
kharifrabi
kharifrabi
kharif
Figure 1. Projected climate change impact on LGP at Solapur.
0
25
50
75
100
75 85 95 105 115 125 135 145 155 165 175 185 195 205Days
Prob
abili
ty o
f Exc
eedi
ng (%
)
Present
Plus 2C and 20% less rain
10 days
20 days
151
rabi
Figure 2. Rain-fed potential yields and yield gaps of selected crops in India.
0
1000
2000
3000
4000
5000
Soybean Groundnut Pigeonpea Chickpea
Yie
ld (
kg
ha
-1)
Max. rainfed potentialMean rainfed potentialDistrict Mean
0
1000
2000
3000
4000
5000
Kharif sorghum Rabi sorghum Pearl millet
Yie
ld (
kg h
a-1)
Max. rainfed potentialMean rainfed potentialDistrict Mean
152
O
153
Weather Forecasting and Advisories
Figure 3. Sorghum yield simulations at Aurangabad, Maharashtra.
13651220
1722
22652163
0
500
1000
1500
2000
2500A
vera
ge g
rain
yie
ld (k
g ha
-1)
Low input
Practices+
Current Climate
1
Low input
Practices+
ClimateChange
2
Improved Practices
+ClimateChange
3
Improved Practices
+Adapted
germplasm+
CurrentClimate
5
Improved Practices
+Adapted
germplasm+
ClimateChange
4
Management and Climate Scenarios
154
ReferencesAllen RG, Pereria LS, Dirk Raes Martin Smith.
Bhatia VS, Singh Piara, Wani SP, Kesava Rao AVR Srinivas K.
Cooper P, Rao KPC, Singh P, Dimes J, Traore PS, Rao K, Dixit P Twomlow SJ.
Kesava Rao AVR, Wani SP, Piara Singh, Rao GGSN, Rathore LS Sreedevi TK.
155
Kesava Rao AVR, Wani SP, Singh Piara, Irshad Ahmed M Srinivas K.
Murty MVR, Piara Singh, Wani SP, Khairwal LS Srinivas K.
Rao GGSN, Kesava Rao AVR, Ramakrishna YS Victor US.
Rego TJ, Sahrawat KL, Wani SP Pardhasaradhi G.
Thornthwaite CW Mather JR.
Wani SP, Maglinao, AR, Ramakrishna A Rego TJ (eds).
Wani SP, Pathak P, Sreedevi TK, Singh HP Singh P.
156
Advances in Geospatial Technologies in Integrated Watershed Management
PS Roy, T Ravisankar and K Sreenivas
AbstractManagement and utilization of natural resources - land and water have assumed the prime importance in the wake of increasing pressure on them. In this context, the watershed approach has gained momentum all over the world for addressing environmental issues and implementing various developmental programs. Though watershed represents a hydrological unit of an area, it is also considered as
management of natural resources. These developmental activities in any watershed, focuses not only on management of rain water, reducing soil loss, runoff and increasing productivity but also focuses
sensing, GPS and GIS are being increasingly used to address various aspects of watershed developmental programs namely preparation of
of critical issues with respect to soils/water/crops, generation of action plans and impact assessment. Several studies were conducted on watershed planning with reference to natural resources and cropping systems planning. Stereo data obtained from aerial and satellite platforms play an immense role in obtaining terrain height information in the watershed. Essentially the height information thus extracted is represented in the form of Digital Elevation Model (DEM). When clubbed with drainage information, hydrological DEM can be generated which inturn is useful to delineate watersheds automatically as well as hydrological modeling of watershed. Besides, high spatial resolution data are increasingly being used to monitor various soil conservation activities, and to assess watershed performance.
During recent years, with the development of communication
collection and transmission, which will be of immense use for real time monitoring of watershed activities. A large network of Automatic
157
Weather Stations across India is being created with state-of-the-art communication tools to serve the data on web in almost real time. The revolution in electronic circuits made it possible to attach a radio to almost any electronic device and remotely communicate with it. This has ushered new ideas of developing sensor web where these sensors can communicate with each other and use the information intelligently as a single system. The sensor network can function independently and collaboratively to provide parameters need to measure in the
nano satellites and their networking with ground based sensors; the data can be used for real time applications in watershed management. Further, Geoinformatics and web GIS tools can bring major impact on the watershed management.
Introduction
Modern Tools for Watershed Management
Remote Sensing
159
Table 1. Suitability of various IRS sensors in watershed studies.
160
161
Geographic Information Systems (GIS)
162
Bhoosampada
Field Sensors and Data Communication Devices
Global Positioning System (GPS)
163
Automatic Weather Station
164
Mobile devices
Cellular phones,
PDA phone with GPS
Data Storage and DisseminationServer technology
165
Internet
Spatial Technologies in Watershed Management
Baseline Studies
166
Watershed characterization
Prioritisation of watersheds
167
Figure 1. Satellite data and DEM for perspective view of watershed in part of Nalgonda district, Andhra Pradesh.
Cartosat data FCC of IRS P6 LISS 4 MSS + Cartosat merged data
Drainage layer draped over DEM generated from Cartosat data
Perspective view 1 of satellite data FCC draped over DEM
Perspective view 2 of satellite data FCC draped over DEM
169
Watershed Monitoring
Technology Integration
170
Field Data Transmission
After implementationBefore implementationPAN + LISS III
Quickbird multi-spectral
ICT’s as viewed by PAN Quickbird satellite
ICT’s as viewed by Multispectral Quickbird satellite
Figure 2. Monitoring microwatershed using high resolution satellite data.
171
Headquarters Field
172
Sensor Web
173
Spatial Simulation Modeling
174
Intelligent Watershed Information System
175
Figure 4. Technology integration for watershed management.
176
Conclusion
ReferencesAnnonynous.
Bharadwaj SP.
Bonde WC. 1985.In
177
Chakraborthy D, Datta D Chandrasekharan H.
.
Diwakar PG Jayaraman V.
Garbrecht J Martz LW.
Khare YD, Srivastara NT, Deshpande AS, Tamhane RM Sinha AK.
In
Khan MA, Gupta VP Moharana PC.
Krishna Murthya YVN, Srinivasa Rao S, Prakasa Rao DS Jayaraman V.
National Remote Sensing Agency.
National Remote Sensing Agency.
National Remote Sensing Agency.
Obi Reddy GP, Maji AK, Srinivas CV Gajbhiye KS.
In
Rao BRM, Sreenivas K, Fyzee MA Ravi Sankar T.
Saxena RK, Verma KS, Chary GR, Srivastava R Barthwal AK.
Sharma T.
Sekhar KR Rao BV.
Sreedevi TK, Wani SP, Kesava Rao AVR, Singh P Ahmed I. 2009.
Srivastava PK, Srinivasan TP, Gupta A, Singh S, Nain JS, Amitabh Prakash S, Kartikeyan B Gopala Krishna B. 2007
www.commission1.isprs.org/hannover07/paper/Srivastava_etal.pdf on 11-Jan-2010.
Thakkar AK Dhiman SD.
.
Wani SP. In
Wani SP, Sreedevi TK, Vamsidhar Reddy TS, Venkateshvarlu B Shambhu Prasad C. 2008.
179
GIS-based Monitoring Systems for Integrated Watershed Management
PG Diwakar1 and SG Mayya2
1
2
AbstractIntegrated Watershed Development (IWD) has been in practice for a very long time and it has received special attention in the recent past with the advent of technological tools for planning, implementation, monitoring and impact studies. IWD itself has gone through varieties of changes in the approaches for development and implementation and so is the case with respect to the possible use of technologies. Of late, the use of space based inputs from remote sensing and Geographic Information System (GIS) technology have helped IWD
in characterizing the terrain, understanding the existing landuse, generating soil maps, estimating ground water/water resource
with base layers and other infrastructure layers. Such an integration of multi-thematic GIS layers with socio-economic data and ground knowledge helps in deriving action plans, which could be a guiding factor for further implementation at grass roots. With the availability of very high resolution satellite images and hence large scale thematic mapping possibilities, it is even possible to address developmental
plans for IWD, but also for systematic monitoring and management. The advantage of using such techniques is in bringing about greater transparency amongst implementers and other stakeholders, which in
farming community.
Close monitoring of IWD programs need many parameters/indicators to be studied in detail throughout the project lifetime. While some of the parameters/indicators are amenable through Management Information System (MIS) databases, the others are obtained through
(villages) through taluk, district and state level users, it is possible to establish web-based information and decision support systems
for closer and effective monitoring of such developmental programs.
plan preparation (also popularly known as DPR preparation at sub-district level in a state), web based GIS and MIS tools for on-line project monitoring and multi-temporal remote sensing data for impact assessment have been successfully used as in watershed development programs of Karnataka. Considering the success achieved in this program, similar attempts are being extended to a few other states in the country. Innovative use of space inputs, information technology and GIS has helped in successfully testing such technologies under operational scenarios. It is now required to emulate more such projects and also integrate such tools and technologies at national level programs like Integrated Watershed Management Program of DoLR,
grass root level farming community.
Introduction
Community Participation in Watershed Development
Study Area
Geomatics as a Solution
Remote Sensing for Planning
Sampling Strategy for Baseline
Geoinformatics for Natural Resources Information
Figure 1. Pictorial view of data collection and analysis.
Figure 2. Geospatial data layers for participatory action plan preparation.
Figure 3. Community level GIS Technology for PRA and action plans.
Web-based Solution for Monitoring
Figure 4a. Package architecture and database synthesis - web-based model.
190
191
Impact Assessment - Space Imaging and Geospatial Analysis
Figure 5. WebGIS tools for online monitoring.
192
Impact
Figure 6. Use of Earth observation in measuring impacts.
193
Conclusion
Figure 7. Social Impacts due to transparency and participatory approach.
194
ReferencesAndreja S Kiristof O.
Chen CF Tsai HT.
Diwakar PG, Ranganath BK Jayaraman V.
Symposium, Jaipur Rajasthan.
Muniyappa NC, Ranganath BK Diwakar PG.
Obermeyer NJ.
Sander C.
195
A Mission to Enhance Productivity of Rain-fed Crops in Rain-fed Districts of Karnataka, India.
KV Raju1, M Babu Rao1, KV Sarvesh1, NC Muniyappa1, Abhijit Dasgupta1 and Suhas P Wani2
1
2
Background
196
Science for Agriculture: ICRISAT and Sujala Watershed Project
(2009).
197
Soil Nutrient Diagnostic Studies
Bridging the Yield Gap
improved management compared to farmers’ management during kharif crop season 2008. (Source: Progress report 2008-09, Sujala-ICRISAT project, 2009).
Goal and Objectives
Strategies
199
taluk
taluk
Figure 2. Organogram of project planning and monitoring mechanism set-up for Bhoochetana project, Karnataka.
200
Table 2. Timeline for execution of activities in Bhoochetana districts.
Map 1. Selected rain-fed districts for crop productivity enhancement under Bhoochetana project in Karnataka.
201
taluks
Rain-fed Agriculture Technologies for Implementation
1. In-situ soil moisture conservation techniques
In-situ
2 Integrated Nutrient Management
202
Rhizobium, Azospirillum
Trichoderma virideRhizobium
Gliricidia
3. Farmers’ preferred varieties
4. Integrated pest management technologies
203
5. Custom hiring centers for agricultural machinery
6. Income-generating rural livelihoods
Project Activities
Capacity Building of Stakeholders
204
taluk
Awareness and Field Publicity Campaigns
taluk
Awareness Building on Soil Nutrient Status
taluks
205
Assisted in Setting up Analytical Laboratory
Scaling-Up Soil, Crop and Water Management Technologies for Boosting Productivity of Selected Crops
Kharif Season Rain-fed Crop Planning 2009
Trichoderma, Azospirillum neem
206
bajra
Table 3. Kharif season cropping planned and actual area sown during 2009 in six districts.
207
Rabi Cropping Targets 2009rabi
rabi rabi
taluk
Table 4. Rabi cropping planned and area of sowing completed in different districts during rabi season 2009.
Field Days
Kharif Season Crop Planning 2010kharif
Results of Participatory Crop Yield Estimates
Crop Season 2009
talukstaluk
209
taluk
talukstaluks
0
500
1000
1500
2000
2500
3000
Pod
yiel
d (k
g ha
-1)
Kol
ar
Chi
kkab
alla
pur
Tum
kur
Chi
trad
urga
Hav
eri
Dha
rwad
Groundnut
Farmers' Management Improved management+Micronutrients
Groundnut pod yield increase (district-wise) with improved management compared to farmers’ management in six districts of Karnataka during kharif 2009.(Source. Annual Progress Report 2009-10, 2010)
210
rabi
0
1000
2000
3000
4000
5000
6000
7000
8000G
rain
Yie
ld (k
g ha
-1)
Kol
ar
Tum
kur
Chi
trad
urga
Chi
trad
urga
Hav
eri
Dha
rwad
Ragi Maize SoybeanCrops
Farmers' Management Improved management+Micronutrients
66%36%
39%
35%
44%
39%
Grain yield increase in selected crops (district-wise) with improved
Karnataka during kharif 2009. (Source. Annual Progress Report 2009-10, 2010)
211
Increased Crop Yields and Economic Gains
improved management compared to farmers’ management under Bhoochetana project, 2009.
212
0
500
1000
1500
2000
Rab
i cro
p se
ed/g
rain
yie
ld (k
g -1)
Sunflower Rabisorghum
Chickpea Chickpea Rabisorghum
Haveri Chitradurga Dharwad
Farmers' Management Improved Management
38%
43%
51%
34%23%
Table 6. Additional income to farmers on additional rupee invested for improved management during 2009 crop season
Grain yield increase in selected crops (district-wise) with improved management compared to farmers’ management in three districts of Karnataka during kharif 2010. (Source. Annual Progress Report, 2010)
213
Crop Season 2010kharif
kharif
0
300
600
900
1200
Gra
in y
ield
(kg
ha-1
)
Bidar Bijapur Yadagir Gadag
Green gram
Farmers' Management Improved Management
38%
52%
31%
57%
0
300
600
900
1200
Gra
in y
ield
(kg
ha-1)
Bidar Bijapur Yadagir Gadag
Green gram
Farmers' Management Improved Management
38%
52%
31%
57%
Grain yield increase in green gram (district-wise) with improved management compared to farmers’ management in four districts of Karnataka during kharif 2010. (Source. Annual Progress Report, 2010)
214
Conclusion
kharif rabi
215
Acknowledgements
taluk
ReferencesSahrawat KL, Wani SP, Rego TJ, Pardhasaradhi G Murthy KVS.
216
Wani SP, Sreedevi TK, Rockström J Ramakrishna YS.
Singh P, Aggarwal PK, Bhatia VK, Murthy MVR, Pala M, Oweis T, Benli B, Rao KPC Wani SP.
Progress Report 2008-09,
Annual Progress Report 2009-10
ICRISAT 2009.
.
ICRISAT ,
217
Application of Meta-analysis to Identify Drivers for the Success of Watershed Programs
PK Joshi1, Suhas P Wani2, KH Anantha2 and AK Jha3
1
2
3
Introduction
21
219
Approach
220
1
221
Watershed (%)
0.6
67.5
13.2 12.22.6 3.9
0.010.020.030.040.050.060.070.080.0
<1 1 to 2 2 to 3 3 to 4 4 to 5 >5
Benefit-cost ratio
Wat
ersh
eds
(%)
222
1.9
30.2
41.4
8.611.1
6.8
0.05.0
10.015.020.025.030.035.040.045.0
<10 10 to 20 20 to 30 30 to 40 40 to 50 >50
Internal rate of return (%)
Wat
ersh
eds
(%)
Figure 2. Distribution (%) of watersheds according to internal rate of return.
223
2
224
225
to people's participation
226
to income status of the region.
227
Drivers for the Success of Watershed Programs
’
People’s Participation
illa parishad
229
Bottom-up Approach
230
in-situ
Knowledge-Based Entry Point Activity
231
Targeted Activities for Women and Vulnerable Groups
Watershed Institutions/Self-Help Groups
232
gram panchayat
Mitra Kisan‘Gopal Mitra
Decentralize Decision-Making Process
233
Capacity Building
234
Demand Driven Watershed Approach
235
Target Poor Regions
Conclusion
236
ReferencesAhluwalia MS.
Department of Land Resources (DoLR).
Deshpande RS Thimmaiah G.
Dixit Sreenath, Tewari JC, Wani SP, Vineela C, Chaurasia AK Panchal HB.
237
Fan S and Hazell P.
Farrington J, Turton C James AJ (eds.).
Farrington J Lobo C.
Government of India.
Government of India.
Hanumantha Rao CH.
Joshi PK, Wani SP, Chopde VK Foster J.
Joshi PK, Vasudha Pangare, Shiferaw B, Wani SP, Bouma J Scott C.
Joshi PK, Jha AK, Wani SP, Joshi Laxmi Shiyani RL.
Joshi PK, Jha AK, Wani SP, Sreedevi TK Shaheen FA.
Kerr J, Pangare G, Pangare LV George PJ.
Meinzen-Dick R, DiGregorio M McCarthy N. Agricultural Systems
Rockström J, Nuhu Hatibu, Theib Y, Oweis Wani SP. in
Samra JS.
Seeley J, Menaakshi Batra Madhu Sarin.
Sharma Rita.
Shiferaw B, Bantilan C Wani SP.
Sreedevi TK, Shiefaw B Wani SP.
Sreedevi TK Wani SP.
239
Sreedevi TK, Wani SP, Sudi R, Patel MS, Jayesh T, Singh SN Tushar Shah.
Sreedevi TK, Wani SP Pathak P.
Wani SP, Pathak P, Tam HM, Ramakrishna A, Singh P Sreedevi TK. 2002.
Wani SP, Singh HP, Sreedevi TK, Pathak P, Rego TJ, Shiferaw B Iyer SR.
in
Wani SP, Pathak P, Jangawad LS, Eswaran H Singh P.
Wani SP Ramakrishna YS.
Wani SP, Ramakrishna YS, Sreedevi TK, Long TD, Thawilkal Wangkahart, Shiferaw B, Pathak P Kesava Rao AVR.
240
Wani SP Sreedevi TK.
Wani SP, Sreedevi TK, Rockstrom J, Wangkahart T, Ramakrishna YS, Yin Dxin, Kesava Rao AVR Zhong Li.
Wani SP, Joshi PK, Raju KV, Sreedevi TK, Mike Wilson, Amita Shah, Diwakar PG, Palanisami K, Marimuthu S, Ramakrishna YS, Meenakshi Sundaram SS Marcella D’Souza.
Wani SP, Sreedevi TK, Rockström J Ramakrishna YS.
241
Hydrological Modeling of a Micro Watershed using GIS-based Model SWAT: A Case Study of
Kothapally Watershed in Southern IndiaKaushal K Garg and Suhas P Wani
AbstractRain-fed agriculture in arid or semi arid tropics is complex, diverse, risk prone, and characterized by low levels of productivity and low input usages. Kothapally, a micro watershed of 450 ha area is located approximately 25 km upstream of Osman Sagar in Musi catchment of Southern India. Rainfall in this region is highly erratic both in terms of total amount and its distribution over time. ICRISAT consortium with national partners (Central Research Institute for Dryland Agriculture (CRIDA), National Remote Sensing Agency (NRSA) now NRSC, and District Water Management Agency (DWMA), in Hyderabad, Andhra Pradesh,); and non-governmental organizations (NGOs) started community based watershed development program in Kothapally
parameters in the area have been monitored, creating database of hydrological data and crop yield information. This data was analyzed with the Soil and Water Assessment Tool (SWAT) to study the water
downstream impacts on the Osman Sagar reservoir. It was found
the water balance of the system. Check-dams increase groundwater recharge which can be used for supplementary irrigation of the monsoon crop, and especially the second crop when rainfall is almost nil. Both check-dams and in-situ soil water management reduces the
evapotranspiration, which can be expected when more water is
management practices reduced surface runoff from 27% to 11%,
from 53% to 66% of total rainfall, and reduced soil loss from 1.5 t ha-1 to 2.5 t ha-1 compared to pre-development stage. This program has built resilience in the agricultural systems, and has improved the livelihoods of the farmers.
Keywords: Hydrological modeling, SWAT, water balance, sediment transport, resilience, watershed management.
242
Introduction
Study Area: Kothapally Watershed
243
Figure 1. (A) Location of Kothapally watershed in Musi sub-basin of Krishna river basin, down stream reservoirs and Hyderabad city; (B) Stream network, location of storage structures, open wells, meteorological station, and residential area in Kothapally watershed.
244
Sarpanch Panchayati Raj
245
Gliricidia
246
Methodology
Input Data and Model Setup
3
Model Calibration
247
Scenario Development
2
Results
Water Balance of Different Water Intervention Scenarios
Sediment Transport and Soil loss
Figure 2. Water balance for the four different water management scenarios for -20%
0%
20%
40%
60%
80%
100%
Scenario1 Scenario2 Scenario3 Scenario4
Per
cent
age
of T
otal
rain
fal
Outflow GW recharge ET Change in Soil MC
Figure 3. Soil loss in different micro-watersheds of Kothapally area in year 2000. Gray colour in map shows soil loss and crossed lines shows its deposition (also shown by negative numbers).
249
watershed development).
0
1020
3040
50
6070
8090
100
0 50 100 150 200 250 300
Daily rainfall (mm)
Soil
loss
(ton
/ha)
Scenario-1
Scenario-4
250
Discussion
Water Management Interventions Improve the Resilience of Small-scale Tropical Agricultural Systems
The Choice of Water Management Intervention Depends on Hydro-ecological and Social Settings
251
Conclusions
252
ReferencesImmerzeel WW, Gaur A Zwart SJ.
Reddy VR, Shiferaw B, Bantilan MCS, Wani SP Sreedevi TK.
Sreedevi TK, Shiferaw B Wani SP.
253
New Tools for Monitoring and Modeling Hydrological Processes in Small Agricultural
WatershedsPrabhakar Pathak, R Sudi and Suhas P Wani
Introduction
254
Soil Loss Measurement from Agricultural Watersheds
Manual Sampling
255
Figure 1. Sediment concentration variation with time during two runoff events at BW7 watershed, ICRISAT Center, Pantancheru, Andhra Pradesh, India (Pathak et al. 2004)
256
Sediment Samplers
Clock-based automatic sediment sampler
Figure 2. Clock-based automatic sediment sampler for small agricultural watersheds
Depth integrating sediment sampler
Working principle and details:
257
Figure 3. Depth integrating sediment sampler for small agricultural watersheds
Figure 4. Schematic diagram of the depth integrating sediment sampler
Figure 5. Working principle of the sediment sampler
259
St = V0 (Vs0 Cs0 – Vs1 Cs1) / (Vs0 – Vs1) + V1 (Vs1 Cs1 – Vs2 Cs2) / (Vs1 – Vs2) + Vn–1(Vsn–1 Csn–1 – Vsn Csn) / (Vsn–1 – Vsn) + Vn Csn
Vs0, Vs1, Vs2, ..., Vsn Cs0, Cs1, Cs2, ..., Csn
M0, M1, M2, ...., Mn
V0, V1, V2, ..., Vn–1, VnOO’1O1 + P1P’1P0, O1O’1O’2O2 + P2P’2P’1P1, O2O’2O’3O3 +
P3P’3P’2P2, ..., On–1 O’n–1O’nOn + PnP’nP’n–1Pn–1, OnO’nP’nPn.
V0, V1, V2, ..., VnVs0, Vs1, ..., Vsn Cs0, Cs1, ..., Csn
M0, M1, ..., Mn
Microprocessor-based Automatic Sediment Sampler
260
Figure 6. Microprocessor based automatic sediment sampler.
261
Salient features of the sediment sampler
Figure 7. Single and multiple level sensors of control unit
Single level sensor Multiple level sensor
262
Runoff Measurement from Small Agricultural Watersheds
Mechanical Stage-level Recorder
Figure 8. A drum-type mechanical runoff recorder.
263
Digital Automatic Stage-level Recorder (Thalimedes)
Figure 9. Digital runoff recorder
264
Integrated Digital Runoff Recorder and Sediment Sampler Device
Figure 10. Integrated digital runoff recorder and sediment sampler device (new microprocessor is shown in inset).
265
Hydrological Models for Agricultural Watersheds
Simple Runoff and Water Balance Models
RUNMOD Runoff Model
266
SCS Curve Runoff Model
267
Figure 11. Flow chart of SCS curve runoff model (Pathak et al. 1989).
Runoff Water Harvesting Models
Figure 12. Runoff model comparison of measured and simulated daily runoff in two Vertisol watersheds at ICRISAT Center, Patancheru, Andhra Pradesh.
269
270
Rainfall
Water stored in tank
Seepage
Runoff OutflowEvaporation
Kacharam
0
20
40
60
80
100
8 12 16 20 24 28 32 36 40 44 48 52
Standard w eeks
Pro
babi
lity
%
20 mm
40 mm
60 mm
100 mm
Figure 15. Probabilities of obtaining 20, 40, 60 and 100 mm cumulative runoff in Kacharam watershed (based on 26 years of simulated data).
271
Digital Terrain Model (DTM)
272
Figure 17. Runoff at day 1 and its ponding at landscape level.
00.20.40.60.811.21.41.61.822.22.42.62.833.2
Figure 16. Slope levels in a landscape.
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
55.00
60.00
65.00
Runoff on Day 1
273
Summary
274
ReferencesAjay Kumar.
Bruno Basso, Ritchie J T Pathak P.
GoI (Government of India)
Krishna JH.
Pathak P Sudi R.
Pathak P, Laryea KB Sudi R.
Pathak P, Wani SP, Singh Piara Sudi R.
Pathak P.
275
Pathak P. In
Sireesha P.
276
Development in Integrated Watershed Development
P Biswabandhu Mohanty
AbstractThe enterprise of rain-fed agriculture is perceived as highly risky due to vagaries of nature, i.e., wide variation in quantum and distribution of rainfall. Therefore, farmers have, over the time, evolved and adopted a ‘low risk and low return’ strategy. This high risk of rain-fed agriculture and low risk-bearing ability of rain-fed farmer is a major issue in rain-fed agriculture.
Moreover, bankers are not comfortable to lend to rain-fed agriculture because of the high percentage of bad loans, and they call this non-performing assets (NPAs). This is a risk cost to the bankers.
NABARD had initiated efforts to promote credit in rain-fed areas
NABARD has been involved in implementation of watershed projects as a project holder under the Indo-German Watershed Development Programme (IGWDP) in Maharashtra since 1992. SHG-Bank Linkage Programme, which was launched by NABARD in 1992, is the
86 million rural households through 6.1 million SHGs. The program has become a national movement and has the potential and promise
services and promoting livelihoods. The rain-fed zones, tribal and forest areas, watershed areas, drylands, hilly tracks, etc., are the testing grounds for the bankers and development agencies for
Introduction
277
Importance of Rain-fed Agriculture
279
NABARD and Watershed Projects
Projects under Watershed Development Fund (WDF)
Integrated Watershed Development Program-Bihar
Indo German Watershed Development Program (IGWDP)
IGWDP - Maharashtra
IGWDP - Andhra Pradesh
IGWDP - Gujarat
IGWDP - Rajasthan
Issues Related to Credit Flow in Rain-fed AreasRisk to farmer:
Risk to banker:
High transaction cost of banks:
NABARD’s Efforts in Credit Flow in Rain-fed Areas
a priori
SHG – Formation and Linkage
Joint Liability Groups (JLGs) – Bank Linkage
Broad Approaches & Focus of NABARD
Suggestions/Recommendations for Rain-fed Agriculture
Dryland Agriculture Technology
Risk Mitigation
Micro Enterprises & Marketing
Micro Financial Services & Delivery Mechanism
micro-enterprises
290
Perspectives
291
ReferencesAnnual Report, NABARD
Policy circulars/papers of NABARD on the relevant issues.
292
Impact of Climate Change on Dryland Sorghum in India
K Boomiraj1, Suhas P Wani2 and PK Aggarwal3
1
2
3
AbstractThis paper presents results of climate change impacts on sorghum in semi arid tropics (SAT) regions of India and adaptation strategies to overcome the impact. The main objective of the paper is how to use crop simulation model to assess the climate change impact and how best we can reduce the impact through integrated watershed approach. InfoCrop, a generic dynamic crop model, provides inte-grated assessment of the effect of weather, variety, pests, and soil management practices on crop growth and yield, on soil nitrogen and organic carbon dynamics in aerobic, anaerobic conditions, and also greenhouse gas emissions. The model has reasonably predicted phenology, crop growth yield. Sorghum crop was found to be sensitive to changes in carbon dioxide (CO2) and temperature. Future climate change scenario analysis showed that sorghum yields (CSH 16 and CSV 15) are likely to reduce at Akola, Anantpur, Coimbatore and Bijapur. But yield of CSH 16 will increase little in Gwalior (0.1%) at 2020 and there after it will reduce. At Kota, the sorghum yield is likely to increase at 2020 (3.3 & 1.7 % in CSH 16 and CSV 15, respectively) and no change at 2050 and yield will reduce at 2080 in both varieties. The increase in yield at Gwalior and Kota at 2020 will be due to reduction in maximum temperature and increase in rainfall from the current. Adoption of adaptation measures like one irrigation (50mm) at 40-45 days after sowing would be better for rain-fed kharif sorghum in the selected location of the SAT regions. The yield gap between district average and simulated rain-fed potential is so wide at Akola, Anantpur, Bijapur and Kota compared with Coimbatore and Gwalior. If we bridge the yield gap, we can overcome the climate change impact. Integrated Genetic and Natural Resource Management (IGNRM) through watershed management would be an appropriate method to bridge the yield gap to sustain the sorghum yield and food security.
Key wordsLeaf area index, Maturity, India, SAT.
293
Introduction
Table 1. Area, production and productivity of sorghum in India compared with rest of the world.
294
kharif
kharif
et al
et al
295
2
et al
kharif
2kharif
et al.
2
et al
296
Materials and Methods
Model
2
4 2
et al
297
Climate Change Impact AssessmentClimate change scenarios
3
kharif
Table 2. Projected mean temperature rise (°C) and rainfall changes during sorghum growing season in A2a scenarios.
Adaptation Strategies
Results and Discussion
Impact Assessment
CSH 16 –Sorghum hybrid
Without adaptation CSH - 16
-35.0-30.0
-25.0-20.0
-15.0-10.0
-5.00.0
5.010.0
% lo
ss in
yie
ld
At2020
At2050
At2080
Akola Anantpur Coimbatore Gwalior Bijapur Kota
With Adaptation CSH - 1
-25.0-20.0-15.0-10.0-5.00.05.0
10.015.020.025.0
Ako
la
Ana
ntpu
r
Coi
mba
tore
Gw
alio
r
Bija
pur
Kot
a
% G
ain
in G
rain
yie
ld
At2020
At2080
Figure 1. Simulated per cent change in yields (CSH 16) in HadCM3 – A2a scenarios of climate change without and with adaptation.
299
CSV 15 Sorghum variety:
kharif °
°
Table 2. Projected mean temperature rise (°C) and rainfall changes during sorghum growing season in A2a scenarios
300
Without adaptation CSV - 15
-35.0
-30.0
-25.0
-20.0
-15.0
-10.0
-5.0
0.0
5.0
% lo
ss in
yie
ld
At2020
At2050
At2080
Akola Anantpur Coimbatore Gwalior Bijapur Kota
With Adaptation CSV - 15
-20.0
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
Ako
la
Ana
ntpu
r
Coi
mba
tore
Gw
alio
r
Bija
pur
Kot
a
% G
ain
in G
rain
Yie
ld
At2020
At2050
At2080
Figure 2. Simulated per cent change in yields (CSV 15) in HadCM3- A2a scenarios of climate change without and with adaptation.
301
et al
Adaptation Strategies
302
Yield Gap
et al
Summary and Conclusion4
2
2
303
Acknowledgement
ReferencesAggarwal PK.
. 78
Aggarwal PK, Kalra N, Chander S and Pathak H.
89
Aggarwal PK, Banerjee B, Daryaei MG, Bhatia A, Bala A, Rani S, Chander S, Pathak H Kalra N.
89
Dayakar Rao B.
Easterling WE, Aggarwal PK, Batima P, Brander, KM, Erda L, Howden SM, Kirilenko A, Morton J, Soussana JF, Schmidhuber J, Tubiello FN. In:
Gangadhar Rao D, Katyal J C, Sinha S K and Srinivas K.
IPCC.
304
Long SP, Ainsworth EA, Leakey ADB, Nosberger J Ort TR. 2
.
Long SP, Ainsworth EA, Leakey ADB, Morgan PB. 2
Morison JIL.
In
Murthy MVR, Piara Singh, Wani SP, Khairwal IS Srinivas K.
Pidgeon JD, Werker AR, Jaggard KW, Richter GM, Lister DH Jones PD,
Rawson HM. 2
Saseendran SA, Singh KK, Rathore LS, Singh SV Sinha SK.
Climate Change
Singh M, Kalra N, Chakraborty D, Kamble K, Barman D, Saha S, Mittal RB Pandey S.
Van Kraalingen DWG.
Wani SP, Sreedevi TK, Rockstrom J Ramakrishna YS.
305
Glimpses of the Workshop
306
Part
icip
ants
of N
atio
nal S
ympo
sium
on
Use
of H
igh
Scie
nce
Tool
s in
Inte
grat
ed W
ater
shed
Man
agem
ent
307
Program
Monday 1 February 2010
Session 1 Inaugural Session
Session 2 Technical Session IChair : PK AgarwalRapporteur : P Pathak
Session 3 Technical Session IIChair : VN ShardaRapporteur : Kaushal K Garg
Tuesday 2 February 2010
Session 4 Technical Session IIIChair : K PalanisamiRapporteur : AVR Kesava Rao
Health Break
309
Lunch
Session 5 Technical Session IVChair: Basu ChinmayRapporteur: K Boomiraj
Group I –
Session 6 Plenary Session Chair: Rita Sinha Rapporteur: P Pathak
310
311
List of Invited Participants
Abey George
Alok Kumar Sikka
Anilan R
Anwar Hussain
Arora VM
312
Arti Chaudhary : : :
Barah BC:
Bhabani Sankar Das
Bharat R Sharma
Boomiraj K
Chinmay Basu
313
Das NK
Diwakar PG
Gobindh Singh
Gopi S
Iain A Wright
Jagpal Singh
314
Joshi PK
: : :
Kasturi Basu
Kausalya Ramachandran
Kesava Rao AVR*
Laxman
315
Majhi EK
Mani Kant Giri
Mohammed Ikbal
Mishra GK
Mhathung Yanthan
Mohd Haleem Kham
316
Mohanty BB
Monideep Chutia
Nagesh Kumar Anumala
Neise Mich
Palanisami K
317
Patrick Jasper
Prabhaker Saraswat
Pramod Kumar Aggarwal
Prem Narain Mathur 25849000
Raj K Gupta
Rajaram R
Rajeev Sharma
Rajesh Bhandari
Rajput BS
Ram Kumar
Reddy CP
319
Rita Sinha
Rita Teotia
Roy PS
Sadamate VV
Sailendra Narayan Naik
Sashidhar
320
Savita Anand
Sharda VN
Sharma TK
Sherawat KS :
Singh AK
Singh KK
321
Sirisha Nemani
:
Surendra Kumar
Suvendu Rout
Thubru KS
Udayabhanu Prakash V
Ugrasen Sahi
322
Umakanth Umrao
Venkateswarlu B
Virinder Sharma
Vivek Dave
ICRISAT Staff
Arun Pal
323
Dar WD
Kaushal K Garg
Pathak P
Prabhat Kumar
Rex L Navarro
Ruchi Srivastava
Satyanarayana KNV
Wani SP
Organizing CommitteeCo-Chairs SP Wani
Prabhat Kumar
Members P PathakKaushal GargArun PalKNV Satyanarayana
Secretarial Support
Y Prabhakara RaoJyoti SharmaN Sri Lakshmi
Citation: Wani SP, Sahrawat KL and Kaushal K Gard (eds.). 2011. Use of High Science Tools in Integrated Watershed Management. Proceedings of the National Symposium, 1–2 Feb 2010, NASC Complex, New Delhi, India. Patancheru 502 324, Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics for the Semi-Arid Tropics. ISBN 978-92-9066-540-3. CPE 169. 328 pp.
© International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), 2011. All rights reserved.
ICRISAT holds the copyright to its publications, but these can be shared and duplicated for non-commercial purposes. Permission to make digital or hard copies of part(s) or all of any publication for non-commercial use is hereby granted as long as ICRISAT is properly cited. For any clarification, please contact the Director of Communication at [email protected]. ICRISAT’s name and logo are registered trademarks and may not be used without permission. You may not alter or remove any trademark, copyright or other notice.
AcknowledgementWe sincerely thank Department of Land Resources (DoLR), Ministry of Rural Development, Government of India, for sponsoring the symposium. We are grateful to National Bank for Agriculture and Rural Development (NABARD), Sir Dorabji Tata Trust (SDTT), Sir Ratan Tata Trust (SRTT) for co-sponsoring the event. We thank the help of Mr Prabhat Kumar, Director, Business and Country Relations, ICRISAT Liaison Office, for coordinating the workshop. We thank Ms N Shalini for language editing; Mr KNV Satyanarayana, Mr Arun Pal and Ms Jyothi for administrative support; Mr Y Prabhakar Rao and Ms N Sri Lakshmi for logistical support; and Communication Office, ICRISAT for production of this report.
ISBN: 978-92-9066-540-3 CPE 169 241-2011
Use of H
igh Science Tools in Integrated W
atershed Managm
ent
Contact InformationICRISAT-Patancheru(Headquarters)Patancheru 502 324Andhra Pradesh, IndiaTel +91 40 30713071Fax +91 40 [email protected]
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About ICRISAT
www.icrisat.org
The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) is a non-profit, non-political organization that conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. Covering 6.5 million square kilometers of land in 55 countries, the semi-arid tropics have over 2 billion people, and 644 million of these are the poorest of the poor. ICRISAT and its partners help empower these poor people to overcome poverty, hunger, malnutrition and a degraded environment through better and more resilient agriculture.
ICRISAT is headquartered in Hyderabad, Andhra Pradesh, India, with two regional hubs and four country offices in sub-Saharan Africa. It belongs to the Consortium of Centers supported by the Consultative Group on International Agricultural Research (CGIAR).
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Use of High Science Tools in Integrated Watershed ManagementProceedings of the National Symposium
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